The Use of an Improved LSSVM and Joint Normalization on Temperature Prediction of Gearbox Output Shaft in DFWT

Abstract

Abstract:In the working process of Double-Fed Wind Turbines (DFWT), it is very important to monitor and predict the temperature of the high-speed output shaft of the gearbox timely and effectively. Support vector machine has more advantages in the temperature prediction of wind turbines. Least squares support vector machine is suitable for online prediction due to reducing the computational complexity of support vector machine. In order to solve the sparsity of least squares support vector machine, an improved least squares support vector machine based on pruning algorithm is proposed in this paper to predict the temperature of the high-speed output shaft of gearbox using the practical data of Double-Fed Wind Turbines. At the same time, in order to improve the prediction accuracy and to solve the problem of few links between different feature parameters in common normalization method, the paper uses the method of joint normalization to preprocess the data. The principal component analysis is used to reduce the dimension of the data. Particle swarm optimization algorithm is used to optimize the parameters of the pruning least squares support vector machine. The proposed model that is established in this paper is a new model to forecast the temperature of the high-speed output shaft. The results show that its prediction accuracy is higher than that of other algorithms.

A Multi-Factor Driven Model for Locomotive Axle Temperature Prediction Based on Multi-Stage Feature Engineering and Deep Learning Framework

“…Abdusamad proposed multiple linear regression (MLR) in future temperature forecasting [19]. Xiao et al conducted experiments on output shaft gearbox temperature forecasting by the least-square support-vector machine (LSSVM) [20].…”